Custom and Animated Maps
Angela Li
2/19/2019
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Load packages
library(sf)
library(ggplot2)
library(spData)
library(tmap)
library(magick)Look at data
head(urban_agglomerations)## Simple feature collection with 6 features and 9 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: -74.00366 ymin: 34.67583 xmax: 139.6917 ymax: 55.755
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs
## # A tibble: 6 x 10
## index year rank_order country_code country_or_area city_code
## <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 1 1950 1 840 United States of America 23083
## 2 2 1950 2 392 Japan 21671
## 3 3 1950 3 826 United Kingdom 22860
## 4 4 1950 4 392 Japan 206459
## 5 5 1950 5 250 France 20985
## 6 6 1950 6 643 Russian Federation 22299
## # ... with 4 more variables: urban_agglomeration <chr>, note <chr>,
## # population_millions <dbl>, geometry <POINT [°]>str(urban_agglomerations)## Classes 'sf', 'tbl_df', 'tbl' and 'data.frame': 510 obs. of 10 variables:
## $ index : num 1 2 3 4 5 6 7 8 9 10 ...
## $ year : num 1950 1950 1950 1950 1950 1950 1950 1950 1950 1950 ...
## $ rank_order : num 1 2 3 4 5 6 7 8 9 10 ...
## $ country_code : num 840 392 826 392 250 643 32 840 356 156 ...
## $ country_or_area : chr "United States of America" "Japan" "United Kingdom" "Japan" ...
## $ city_code : num 23083 21671 22860 206459 20985 ...
## $ urban_agglomeration: chr "New York-Newark" "Tokyo" "London" "Kinki M.M.A. (Osaka)" ...
## $ note : chr "…" "1" "2" "3" ...
## $ population_millions: num 12.34 11.27 8.36 7.01 6.28 ...
## $ geometry :sfc_POINT of length 510; first list element: Classes 'XY', 'POINT', 'sfg' num [1:2] -74 40.7
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA
## ..- attr(*, "names")= chr "Index" "Year" "Rank order" "Country code" ...names(urban_agglomerations)## [1] "index" "year" "rank_order"
## [4] "country_code" "country_or_area" "city_code"
## [7] "urban_agglomeration" "note" "population_millions"
## [10] "geometry"tail(urban_agglomerations)## Simple feature collection with 6 features and 9 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: -118.2417 ymin: -12.04318 xmax: 114.0634 ymax: 34.03166
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs
## # A tibble: 6 x 10
## index year rank_order country_code country_or_area city_code
## <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 505 2030 25 360 Indonesia 21454
## 2 506 2030 26 840 United States of America 23052
## 3 507 2030 27 586 Pakistan 22046
## 4 508 2030 28 356 India 21275
## 5 509 2030 29 156 China 20667
## 6 510 2030 30 604 Peru 22078
## # ... with 4 more variables: urban_agglomeration <chr>, note <chr>,
## # population_millions <dbl>, geometry <POINT [°]>Looks like urban_agglomerations is a panel-data version of population of global cities, between 1950 and 2030.
Make a ggplot2 map
ggplot(urban_agglomerations) +
geom_sf()
ggplot(world) +
geom_sf()
ggplot() +
geom_sf(data = world) +
geom_sf(data = urban_agglomerations)
Move it over to tmap!
Single custom map
tm_shape(world) +
tm_polygons() +
tm_shape(urban_agglomerations) +
tm_dots(size = "population_millions", title.size = "Population (m)", col = "red")
Map with small multiples
tm_shape(world) +
tm_polygons() +
tm_shape(urban_agglomerations) +
tm_dots(size = "population_millions", title.size = "Population (m)", col = "red") +
tm_facets(by = "year")
Map (animated!!)
tm <- tm_shape(world) +
tm_polygons() +
tm_shape(urban_agglomerations) +
tm_dots(size = "population_millions", title.size = "Population (m)", col = "red") +
tm_facets(along = "year", free.coords = FALSE)
tmap_animation(tm, "city-pop-anim.gif", loop = TRUE, delay = 80)## Animation saved to /Users/angela/Desktop/Spatial Data Science/workshop-scripts/doc/city-pop-anim.gifmagick::image_read("city-pop-anim.gif")